Positive Spatial Correlation Research Articles

The impact of R&D factors flow and regional absorptive capacity on China's economic growth: Theory and evidence.

Innovation is the source of economic growth. Innovation in a region comes from its own knowledge creation and knowledge spillovers from other regions. Previous studies showed that R&D factors flow benefits knowledge spillover, thereby promoting economic growth. But these studies ignored the impact of a region's knowledge-absorptive capacity on knowledge spillovers. Ignoring the impact of regional absorptive capacity means that the knowledge spillover from the same R&D factors flow is the same, clearly inconsistent with reality. This thesis analyzes the impact of R&D factors flow on economic growth and explores the moderating effect of regional absorptive capacity on the relationship between R&D factors flow and economic growth from theoretical and empirical perspectives. First, we construct a knowledge creation and diffusion model of the new economic geography, including regional absorptive capacity, and analyze the theoretical logic of the flow of R&D factors and regional absorptive capacity influencing economic growth. Second, we employ spatial econometric models to examine the impact of R&D factors flow and regional absorptive capacity on economic growth, utilizing panel data of 30 provinces in China from 2008 to 2021. The results demonstrated a spatial positive correlation between regional economic growth in China. The R&D factors flow could have significantly promoted not just a region's direct economic growth, but also the economic growth of surrounding regions via spatial spillover effects. Furthermore, the stronger the regional absorptive capacity, the greater the direct effects and spatial spillover effects of the R&D factors flow on economic growth. The novelty of this article is to introduce regional absorptive capacity into the theoretical model, refine the methodology for assessing regional absorptive capacity in empirical research, and examine its moderating effect between the inflow of R&D factors and regional economic growth. This article reveals that the positive impact of the inflow of R&D factors on spatial spillovers and economic growth varies depending on regional absorptive capacity. According to the conclusions above, enhancing regional absorptive capacity is equally important as facilitating the flow of R&D factors. Therefore, it is vital for a region to strengthen its absorptive capacity for new knowledge while promoting R&D factors flow. The study provides valuable policy insights for accelerating the flow of innovation factors, enhancing regional absorptive capacity, and consequently promoting long-term sustainable economic development in the region.

Spatiotemporal Evolution, Driving Mechanisms, and Zoning Optimization Pathways of Ecosystem Health in China

The health status of ecosystems is an important prerequisite for ensuring regional ecological security. Exploring the spatiotemporal patterns, driving mechanisms, and zoning regulation pathways of ecosystem health is of great significance for achieving co-ordinated and sustainable regional ecosystems. This study uses China as a case area and applies the InVEST model to measure integrated ecosystem services and incorporates it into an evaluation framework for ecosystem health based on the “Vigor-Organization-Resilience-Ecosystem Services” (VORS) model. It reveals the spatiotemporal evolution characteristics of ecosystem health in China from 2000 to 2020 and employs the geodetector and spatiotemporal geographically weighted regression model to analyze the main influencing factors and spatial differentiation characteristics, thereby exploring ecological management zoning and optimization pathways. The study results show that (1) during the study period, the overall ecosystem health level in China showed a declining trend, dropping from 0.397 in 2000 to 0.377 in 2020. (2) Overall, China’s ecosystem health exhibits strong spatial positive correlation and spatial clustering characteristics, with a basic pattern of lower values in the northwest and higher values in the southeast. (3) Vegetation coverage, population density, density of road network, and per capita GDP are the main influencing factors of ecosystem health in China. (4) China is divided into five types of Ecological Management Zones: Ecological Conservation Zone, Ecological Enhancement Zone, Ecological Buffer Zone, Ecological Remediation Zone, and Ecological Reshaping Zone, with differentiated strategies proposed for optimizing ecosystem health in each zone.

The effects of digital economy on manufacturing resource allocation efficiency across Chinese cities: a spatial economic perspective

ABSTRACT The development of digital economy provides significant impacts on spatial distribution of industries. Using microdata from Chinese manufacturing enterprises and adopting a spatial economic perspective, this study reveals that the digital economy exerts non-linear impacts on resource allocation. As the level of urban digital economy improves, the efficiency of manufacturing resource allocation shows an inverted U-shaped trend, and high hierarchy cities exhibit higher critical values. The study finds a positive spatial correlation in manufacturing resource allocation efficiency across cities; however, the growth of digital economy in neighbouring cities may lead to backwash effects and impede local improvements in allocation efficiency. The mechanism analysis indicates that the digital economy primarily affects spatial resource allocation through urban agglomeration economies and diseconomies. Consequently, economic agents and policy makers in cities should consider the dual effects of ‘location optimization’ and ‘transforming the economy from substantial to fictitious’ during digital transformation processes to select optimal development paths and improve allocation efficiency.

Spatiotemporal Variations in Gross Ecosystem Product and Its Relationship with Economic Growth in Ecologically Vulnerable Watershed Areas: A Case Study of Yongding River Basin

Ecosystem service value is crucial for balancing economic growth and ecological preservation in ecologically vulnerable watershed areas. Although Gross Ecosystem Product (GEP) has received significant attention, most existing studies have focused on how to measure it. Few studies have explored spatiotemporal variations in GEP and how land-use changes affect these variations regarding ecological restoration at the river basin level. Additionally, while many studies have examined the relationship between ecosystem service value and economic growth, there is little research on how components of GEP influence economic growth. Analyzing the spatiotemporal structure of GEP and its components could offer new insights into optimizing ecological restoration strategies and promoting sustainable development in vulnerable watershed regions. In this study, we used ArcGIS, InVEST, SPSS, and Python to analyze spatiotemporal variations in GEP in the Yongding River Basin within the Beijing–Tianjin–Hebei Economic Region from 1995 to 2020. Moran’s Index and variance decomposition were applied to analyze the spatiotemporal structure. The grey prediction model forecasted GEP trends from 2025 to 2035. The random forest model was used to assess land-use changes’ impacts on GEP. Paired T-tests were used to compare GEP and GDP, and a dynamic panel model was used to examine how ecosystem service value factors influenced economic growth. The results show the following: (1) Regarding values, GEP accounting and variance decomposition results indicated that ecosystem cultural service value (ECV) and ecosystem regulating service value (ERV) each contributed about half of the total GEP. Ecosystem provisioning service value (EPV) showed an upward trend with fluctuations. Regarding the spatial distribution, Moran’s I analysis showed significant positive spatial correlations for EPV and ERV. The grey prediction model results indicated significant growth in GEP from 2025 to 2035 under current ecological restoration policies, especially for ERV and ECV. (2) In terms of the influence of land-use changes, random forest analysis showed that the forest land area was consistently the most influential factor across GEP, EPV, and ERV. Unused land area was identified as the most significant factor for ECV. (3) Before 2010, GEP was larger than GDP, with significant differences between 1995 and 2000. From 2010 onwards, GDP surpassed GEP, but the differences were not statistically significant. Dynamic panel regression further showed that the water conservation value significantly boosted GDP, whereas the water purification value significantly reduced it. This study highlights the importance of integrating GEP into ecological restoration and economic development to ensure the sustainability of ecologically vulnerable watershed areas.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Impact of environmental information disclosure on environmental regulation intensity: evidence from 113 cities in China

Presently, the driving factors for environmental regulatory intensity, especially environmental information disclosure, remain unclear. Exploring the causal relationship between the two has an important guiding value for governments to achieve sustainable development. This research empirically examines the impact of environmental information disclosure on environmental regulatory intensity, and the possible action mechanisms and boundary conditions based on panel data of 113 Chinese cities from 2010 to 2020 using fixed-effects model, quantile regression model, moderating effect model, and spatial econometric models. The conclusions suggest that, first, a 1% increase in environmental information disclosure promotes a positive increase in environmental regulatory intensity of about 0.171%. Second, The regression coefficients of environmental information disclosure in the 10th, 25th, 50th, 75th and 90th quartiles are 0.489, 0.401, 0.443, 0.365 and 0.281 respectively, showing a decreasing trend. Third, transportation infrastructure and the resident’s education level exhibit positive interactions, with interaction term regression coefficients of 0.006 and 0.070, respectively; foreign direct investment exhibits a negative interaction, with coefficient of -0.029. Fourth, both environmental regulation intensity and environmental information disclosure show positive spatial correlations, with a 1% increase in environmental regulation intensity and environmental information disclosure in neighboring cities causing an increase in environmental regulation intensity of about 0.734% and 0.026% in this city. This research demonstrates the positive effects of adopting environmental information disclosure on the realization of sustainable development and provides empirical evidence for government policy formulation.

Influence of socioeconomic aspects on morbidity and mortality rates from COVID-19 in the state of Tocantins, Brazil

This study aimed to analyze the spatial correlation of COVID-19 with social vulnerability indices in the state of Tocantins. Methodology: An ecological and cross-sectional study was conducted, analyzing the temporal evolution of the pandemic using the local bivariate Moran's index and the Kernel intensity estimator for COVID-19 cases in Tocantins from 2020 to 2022. Results: The northern region of the state showed the highest concentration of cases and deaths, a concerning fact given the high social vulnerability of most municipalities in this region. Conclusion: The study identified a positive spatial correlation between the incidence and mortality rates of COVID-19 and the social vulnerability index of the municipalities. These findings highlight the need to strengthen preventive measures and expand the physical, technological, and human resources of the healthcare network, especially in areas serving populations with higher vulnerability and traditional communities. This is particularly relevant as the disease also affects more isolated areas, impacting indigenous, riverside, and quilombola populations.

Integrating Resilience Theory Into a Cultivated Land System: Empirical Insights From China's Key Grain‐Producing Regions

ABSTRACTExploring cultivated land use and conservation from a resilience perspective is crucial to address regional food security concerns. This article first examines the theoretical applicability of resilience in the context of cultivated land use systems and subsequently introduces a specific concept of cultivated land system resilience (CLSR). Focusing on the middle reaches of the Yangtze River, a major grain‐producing area in China, the study constructs a measurement system that encompasses five dimensions: endowment of production, socioeconomic factors, scale structure, ecological pressure, and food security. This system is used to evaluate the spatio‐temporal evolution and influencing mechanisms of CLSR in the region from 2001 to 2020. The results indicate that the level of CLSR increased by 7.2% overall and exhibits a distinct spatial pattern, with high‐value areas expanding from the center to the surrounding regions. Furthermore, CLSR shows a positive spatial correlation with rising levels of agglomeration. The spatial heterogeneity of CLSR is influenced by the interaction of various factors, resulting in a partially nonlinear two‐factor enhancement. The influence of each factor varies between different time periods, shifting from ecological pressure to scale structure, ultimately affecting food security. Thus, the resilience perspective offers greater explanatory power to identify key vulnerabilities in the regional cropland system. This approach can improve the efficient allocation of cropland resources and promote sustainable and healthy development at the regional level.

Spatial patterns and its influencing factors on villages around the Ji-shape bend of the Yellow River

Population migration brings about many problems in villages. The Ji (几)-shape bend of the Yellow River is an important ecological sensitivity study area, and some progress exists in the study of villages, still has many problems. This article addresses the inadequacy of the existing research and mainly solves the problems in three areas. It primarily used the research method of Arc GIS10.8, the big data research methods such as imbalance index, Gini coefficient, geographic concentration index, Geodetector, and other methods were used, laying the groundwork for additional analysis of various factors. Hence, the subsequent study findings were attained. 1) The distribution pattern in the village system around the Ji-shape bend of the Yellow River seems to be a positive spatial correlation, with the characteristics of random distribution, uneven spatial distribution, and low concentration, with Hohhot-Baotou serving as a center. 2) Natural factors are the most important factors in the distribution of villages. 3) Among the natural factors, elevation (0.244619), the value of slope (0.319805), and the aspect of slope (0.074089) appear to be the strongest explanatory power. Among the social factors, the rate of urbanization (0.019082), highway density (0.019082), and percentage of tertiary industry (0.018422) show the strongest explanatory power on socioeconomic factors. The natural and social characteristics of villages are important for the siting of villages. This paper put forward some suggestions for the protection and development of villages in ecologically sensitive areas.

Spatiotemporal evolution and driving factors of China's carbon footprint pressure: Based on vegetation carbon sequestration and LMDI decomposition

Evaluating carbon footprint pressure (CFP) scientifically and quantifying the environmental impact of economic activities precisely are crucial for informing policies on carbon emission reduction and achieving carbon neutrality in China. This study revealed China's CFP from 2005 to 2021 using data sets of fossil fuel carbon emissions and vegetation carbon sequestration from remote sensing. We investigated the drivers of CFP using LMDI decomposition. Additionally, we used the Tapio model to examine the decoupling of CFP and economic growth. The findings indicate that China's CFP has increased over time and shows a positive spatial correlation. Rapid economic growth and significant population expansion are the primary drivers of the surge in CFP. Conversely, optimizing energy structure, reducing energy intensity, and enhancing ecological quality are key to alleviating the rising pressure on China's carbon footprint. Moreover, we observe a weak decoupling of China's CFP and economic growth. Notably, it is more challenging to achieve an ideal decoupling state in the western region than in the central and eastern regions.

Direct and Indirect Effects of Environmental and Socio-Economic Factors on COVID-19 in Africa Using Structural Equation Modeling

Understanding direct and indirect relationships of environmental, socio-economic and climate variables and the dynamics of epidemics is key to guiding targeted public health policy and interventions. This study investigates the direct and indirect effects of environmental and socio-economic factors on the COVID-19 dynamics in Africa (54 African countries from 2019 to 2021) using SEM approach. Specifically, the study aimed to: (i) assess the performance of two SEM estimation methods (Lisrel and PLS-SEM) in relationship to sample size (100, 200, 500, and 1000) and level of model complexity (No, two, and four indirect effects) and (ii) use the most performing SEM estimation method to examine direct and indirect effects of factors influencing the number of cases and deaths of COVID-19 in Africa. The results highlight a positive spatial correlation between factors such as temperature, humidity, age, the proportion of people aged over 65, and the COVID-19 incidence. Under the control of confounding factors, Lisrel turns out to be the most performing method, identifying climate, demographic and economic factors as the main determinants of COVID-19 dynamics. These factors have a direct and significant impact on the incidence of COVID-19. An indirect relationship was also observed between economic factors and the incidence of COVID-19 through air pollutants. The results highlight the importance of considering these factors in understanding the spread of the virus to avoid further disasters.

RESEARCH ON DIGITAL ECONOMY AND GREEN TOTAL FACTOR PRODUCTIVITY GROWTH OF CHINA’S INDUSTRY AND SPATIAL EFFECTS

This paper evaluates the impact and role of digital economy on industrial green total factor productivity based on the perspective of China’s industrial green transformation and provides a realistic basis and decision-making reference for industrial green transformation. This paper constructs the industrial green total factor productivity and digital economy development index of 30 provinces from 2013 to 2019, and empirically tests the spatial-temporal evolution relationship between them and the impact of digital economy on industrial green development by using time-fixed effect and spatial Durbin model. The results show that the digital economy and industrial green total factor productivity digital economy are unevenly developed, basically showing a distribution trend of high in the east and low in the west, and the two have a significant positive spatial correlation in the years observed. The results of local spatial autocorrelation analysis show that there is a high clustering of digital economy and total factor productivity in the eastern coastal region and low clustering in the western region. The regional heterogeneity analysis shows that the development of digital economy is beneficial to optimize the distortion of capital and labor allocation and thus improve industrial green total factor productivity, but the indirect effect of digital economy on industrial green total factor productivity is not significant and the direct effect plays a dominant role.

The locust plagues of the Ming Dynasty in Shandong Province, China

In today’s era of unprecedented climate change, research on environmental changes and natural disasters remains crucial. Locust plagues, among the most devastating agricultural disasters, had a profound impact on ancient China, especially during the Little Ice Age. This study utilizes historical records of locust plagues, employing semantic quantification, index construction and spatial analysis, to explore their temporal distribution, spatial patterns, migration characteristics, and associations with climate factors in Shandong during the Ming Dynasty. Key findings include: (1) Locust plagues in Shandong were severe, with 599 documented occurrences, primarily in summer and autumn, especially from April to August. The frequency and intensity of these outbreaks increased significantly during the mid to late Ming Dynasty, peaking in the late Ming period. (2) Geographically, locust plagues were concentrated in the northern river regions of Shandong, particularly in cities like Jinan, Dezhou, and Weifang, with marked clustering, spatial heterogeneity, and positive spatial correlation. (3) Locust plagues were strongly linked to droughts, showing significant temporal and spatial synchrony, but had no significant correlation with floods. These plagues were more likely to occur during cooler and drier periods, with both temperature and precipitation significantly influencing their occurrence.

Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China

BackgroundIn China, the problem of HIV infection among the older people has become increasingly prominent. This study aimed to analyze the pattern and influencing factors of HIV transmission based on a genomic and spatial epidemiological analysis among this population.MethodsA total of 432 older people who were aged ≥ 50 years, newly diagnosed with HIV-1 between January 2018 and December 2021 and without a history of ART were enrolled. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. The molecular transmission network was constructed using HIV-TRACE and the spatial distribution analyses were performed in ArcGIS. The multivariate logistic regression analysis was performed to analyze the factors associated with clustering.ResultsA total of 382 sequences were successfully sequenced, of which CRF07_BC (52.3%), CRF01_AE (32.5%), and CRF08_BC (6.8%) were the main HIV-1 strains. A total of 176 sequences entered the molecular network, with a clustering rate of 46.1%. Impressively, the clustering rate among older people infected through commercial heterosexual contact was as high as 61.7% and three female sex workers (FSWs) were observed in the network. The individuals who were aged ≥ 60 years and transmitted the virus by commercial heterosexual contact had a higher clustering rate, while those who were retirees or engaged other occupations and with higher education degree were less likely to cluster. There was a positive spatial correlation of clustering rate (Global Moran I = 0.206, P < 0.001) at the town level and the highly aggregated regions were mainly distributed in rural area. We determined three large clusters which mainly spread in the intra-region of certain towns in rural areas. Notably, 54.5% of cases in large clusters were transmitted through commercial heterosexual contact.ConclusionsOur joint analysis of molecular and spatial epidemiology effectively revealed the spatial aggregation of HIV transmission and highlighted that towns of high aggregation were mainly located in rural area. Also, we found vital role of commercial heterosexual contact in HIV transmission among older people. Therefore, health resources should be directed towards highly aggregated rural areas and prevention strategy should take critical persons as entry points.

Resilience evaluation and spatio-temporal evolution analysis of China's science and technology financial ecosystem

ABSTRACT This paper discusses the resilience of science and technology (sci-tech) financial ecosystem. It constructs an evaluation system from four dimensions: resistance, adaptability, recovery and innovation. The data are from 29 provinces in China from 2010 to 2020. It is evaluated using entropy weights–TOPSIS and dynamic evaluation model. The spatial evolution law is revealed by spatial autocorrelation analysis. The results show that: (1) The overall resilience level of sci-tech financial ecosystem is not high. Its evolution and development are generally in a relatively stable state. It has significant regional heterogeneity, showing a distribution pattern of ‘high east and low west’. (2) It has significant spatial positive correlation. The structure of the sci-tech financial ecosystem in the eastern region is highly complex, most provinces are in high–high agglomeration area. Provinces in other regions are mainly in low–low agglomeration area. Only a few provinces have experienced changes in the spatial distribution. (3) The dynamic value in the eastern region shows polarisation characteristics. Although the static value of resilience in the central and western regions is low, the dynamic evaluation value in some provinces in the central and western regions is high. The subsequent growth momentum of resilience in the north-eastern region is insufficient.

Spatiotemporal Differentiation of Carbon Emissions from Logistics Industry at Provincial Scale in China Under the Background of High-quality Economic Development

Since 2010, the Chinese economy has transitioned from a high-speed growth model to a high-quality development model. During this period, the logistics industry has witnessed rapid growth, leading to significant carbon emissions and posing severe threats to the ecological environment. To investigate the spatiotemporal variations in carbon emissions in China's logistics industry, we conducted a correlation analysis using Moran's I index and a bivariate spatial autocorrelation model from 2010 to 2021. Additionally, we employed a geographically and temporally weighted regression model （GTWR） to examine the spatial heterogeneity of factors influencing provincial-level logistics-related carbon emissions. The results indicated that over the study period, there was a shift from insignificant spatial relationships to significant positive spatial correlations among provincial-level logistics carbon emissions in China. Furthermore, varying degrees of spatial clustering were observed. The findings regarding factor heterogeneity revealed that freight turnover volume, per capita GDP of the logistics industry, and infrastructure level exhibited positive spatial correlations with logistics-related carbon emissions, whereas energy intensity showed negative spatial correlations with such emissions. Comparing the results from the geographically weighted regression （GWR） and ordinary least squares regression （OLS）, it was evident that the adjusted R-squared values for the OLS, GWR, and GTWR models were 0.541, 0.567, and 0.838, respectively. This suggests that our adopted GTWR model provided a superior fit and offered better explanations for spatiotemporal heterogeneity between various influencing factors and logistics-related carbon emissions. These research findings can serve as valuable references for formulating province-specific strategies to reduce carbon emissions within China's economy under its high-quality development context.

Spatial patterns of maternal and neonatal continuum of care use and its correlations with women’s empowerment

BackgroundThe continuum of care (CoC) in maternal health refers to the continuity of individual reproductive health care across the antenatal, intrapartum, and postnatal periods. The CoC is an indicator of the quality of maternal and newborn health outcomes and women’s empowerment is crucial to improving maternal and neonatal health service access and utilisation.ObjectiveTo examine the spatial patterns of continuum of care use for maternal and neonatal health services and its correlation with women’s empowerment.MethodsWe analysed data from the Ethiopian Demographic and Health Surveys (EDHS) of 2011 and 2016. All women aged 15–49 who had live births in the preceding five years of the DHS surveys were included in the analysis. We measured the continuum of care using the modified co-coverage index (CoCI), which consisted of six indicators. Women’s empowerment was assessed using a validated survey-based Women’s Empowerment (SWPER) index. We used the Getis-Ord-Gi* spatial analysis tool to portray locations with clusters of CoC service use and spatial correlations between CoC use and women empowerment.ResultsNone of the newborn-mother pairs in the 2011 survey received the entire continuum of care and only 2.5% of newborn-mother pairs received the full range of continuum of care services in the 2016 survey. In 2016, 6.9% of mother-newborn pairs received the basic CoC services (four or more antenatal care [ANC] visits, skilled birth attendance [SBA], and postnatal care [PNC]), and no mother-newborn pair received all three services at the same time in 2011. The Amhara, Afar, and Somali regional states had the least CoC service use in both surveys. There was a positive spatial correlation between CoC use and women’s empowerment domains.ConclusionOur analysis showed that the use of four or more ANC visits, SBS, newborn PNC, Bacillus Calmette-Guérin (BCG) vaccine uptake, and tetanus toxoid protection at birth were low in Ethiopia. Women empowerment domains were found to have a positive spatial correlation with CoC services use. To improve and preserve continuity of care, it is critical to leverage every maternal health facility encounter to encourage sustained service usage at each step of the continuum. Government policies should prioritise women’s empowerment and raise public awareness of maternity services.

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China.

To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities. Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen. The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas. Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Research on the coupling coordination of digital economy and urban green development in the Yangtze River Delta of China

Digital economy has become an important engine for promoting green development.The existing literature mainly analyzed the impact of digital economy on green development, but rarely examines the coordinated development relationship between the two. This study contributes to the literature by constructing a research framework for the coupling coordination of digital economy and urban green development, using entropy method, coupling coordination model, and spatial auto-correlation to examine the coordinated development relationship between the two.The results show that:(1) During the research period, the comprehensive level of digital economy and urban green development in the Yangtze River Delta (YRD) both showed an upward and consistent trend. The level of digital economy was significantly lower than that of green development, but the gap between the two continued to narrow. (2) The overall coupling coordination level between digital economy and green development in the 41 cities of YRD was relatively low but showing a continuous upward trend. It has undergone a transition from near imbalance to primary coordination, still with a long way to realize high-quality coordination. (3) The coupling coordination level of digital economy and urban green development in different regions of YRD showed spatial differences, the order from high to low was Shanghai, Zhejiang, Jiangsu and Anhui province respectively. (4) The coupling coordination of digital economy and urban green development in the YRD exhibited significant spatial positive correlation and spatial dependence characteristics. The research results can provide a new perspective to promote the positive interaction between green development and digital economy.

Exploring the spatial relationship between soil quality index and soil ecosystem services driven by social-ecological factors: A peri-urban case study in central China

Urban expansion has profound impacts on soil quality in peri-urban areas. Understanding the intricate spatial relationship between soil quality index (SQI) and soil ecosystem services (SESs) in response to social-ecological factors is crucial for achieving sustainable soil utilization and management. In this study, a total of 485 soil samples were collected from different land-use types (including paddy field, dry land, orchard, and forest) in peri-urban areas of a city (Nanchang) in central China. The assessments of soil quality based on SQI and SESs were conducted using multi-source heterogeneous data. We determined the spatial relationships between SQI and six SESs using bivariate spatial autocorrelation analysis, and deciphered the role of 11 social-ecological factors in driving SQI–SES relationships across different zones. The results showed that SQI and SESs exhibited significant spatial clustering (P<0.01), despite certain variations in their distribution patterns. SQI had significant positive spatial correlations with soil organism habitat, food provision, erosion control, carbon storage, and filtering and buffering (r = 0.16–0.48, P<0.05). Two-step clustering categorized 183 villages with distinct SQI–SES relationships into four zones, i.e., soil ecological protection zone, food production core zone, soil quality enhancement zone, and soil ecological restoration zone. The impacts of various social-ecological factors on different zones were unveiled and corresponding development recommendations were presented. Results of this study facilitate the formulation of peri-urban-specific soil conservation policies and management strategies. Our work provides guidance on the improvement of soil quality and soil ecosystems, contributing to the future achievement of sustainable development goals.